from sklearn.svm import SVC from sklearn.metrics import confusion_matrix # Import data dataset = LoadData("Social_Network_Ads.csv").data # Split the dataset X = dataset.iloc[:, [2,3]].values y = dataset.iloc[:, 4].values # Lets do some preprocessing... processor = PreProcessing() # Split the data X_train, X_test, y_train, y_test = processor.split(X, y, test_size=0.25) # scale the data X_train = processor.fit_scaler(X_train) X_test = processor.scale(X_test) # Lets fit the model now classifier = SVC(kernel='rbf', random_state=0) classifier.fit(X_train, y_train) # Predict! y_pred = classifier.predict(X_test) # Creating the confusion matrix cm = confusion_matrix(y_test, y_pred) cm # Fine, lets visualize it.. I geuss its more fun ЪциРђЇ visual = ClassifierVisual(X_train, y_train, classifier) visual.visualize(title='Linear SVM', xlab='Age', ylab='Salary')
X = dataset.iloc[:, 3:13].values y = dataset.iloc[:, 13].values # Lets do some preprocessing... processor = PreProcessing() # Encode the data (Country/Gender) X[:, 1] = processor.encode(X[:, 1]) X[:, 2] = processor.encode(X[:, 2]) X = processor.hot_encoding(data=X, features=[1]) X = X[:, 1:] # Split the data into training+test X_train, X_test, y_train, y_test = processor.split(X, y, test_size=0.2) # Apply feature scaling X_train = processor.fit_scaler(X_train) X_test = processor.fit_scaler(X_test) # Initialize the Artificial Neural Network (ANN) classifier = Sequential() # Create the input and first hidden layers classifier.add( Dense(input_dim=11, activation='relu', units=8, kernel_initializer='uniform')) # Create the second hidden layer classifier.add(Dense(activation='relu', units=8, kernel_initializer='uniform')) # Create the output layer classifier.add( Dense(activation='sigmoid', units=1, kernel_initializer='uniform'))